Overview

Dataset statistics

Number of variables19
Number of observations4116
Missing cells134
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory611.1 KiB
Average record size in memory152.0 B

Variable types

Categorical1
Numeric18

Alerts

JAN is highly overall correlated with Jan-FebHigh correlation
FEB is highly overall correlated with MAR and 1 other fieldsHigh correlation
MAR is highly overall correlated with FEB and 3 other fieldsHigh correlation
APR is highly overall correlated with MAR and 4 other fieldsHigh correlation
MAY is highly overall correlated with APR and 5 other fieldsHigh correlation
JUN is highly overall correlated with MAY and 6 other fieldsHigh correlation
JUL is highly overall correlated with JUN and 4 other fieldsHigh correlation
AUG is highly overall correlated with JUN and 4 other fieldsHigh correlation
SEP is highly overall correlated with JUN and 4 other fieldsHigh correlation
OCT is highly overall correlated with MAY and 5 other fieldsHigh correlation
NOV is highly overall correlated with OCT and 1 other fieldsHigh correlation
ANNUAL is highly overall correlated with APR and 9 other fieldsHigh correlation
Jan-Feb is highly overall correlated with JAN and 2 other fieldsHigh correlation
Mar-May is highly overall correlated with MAR and 5 other fieldsHigh correlation
Jun-Sep is highly overall correlated with JUN and 5 other fieldsHigh correlation
Oct-Dec is highly overall correlated with APR and 5 other fieldsHigh correlation
SUBDIVISION is highly overall correlated with Jun-SepHigh correlation
SUBDIVISION is uniformly distributedUniform
JAN has 608 (14.8%) zerosZeros
FEB has 645 (15.7%) zerosZeros
MAR has 452 (11.0%) zerosZeros
APR has 219 (5.3%) zerosZeros
MAY has 82 (2.0%) zerosZeros
OCT has 85 (2.1%) zerosZeros
NOV has 651 (15.8%) zerosZeros
DEC has 884 (21.5%) zerosZeros
Jan-Feb has 238 (5.8%) zerosZeros

Reproduction

Analysis started2023-07-28 17:53:11.881986
Analysis finished2023-07-28 17:54:44.698425
Duration1 minute and 32.82 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

SUBDIVISION
Categorical

HIGH CORRELATION  UNIFORM 

Distinct36
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
WEST MADHYA PRADESH
 
115
EAST RAJASTHAN
 
115
COASTAL KARNATAKA
 
115
TAMIL NADU
 
115
RAYALSEEMA
 
115
Other values (31)
3541 

Length

Max length34
Median length19
Mean length15.427114
Min length5

Characters and Unicode

Total characters63498
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowANDAMAN & NICOBAR ISLANDS
2nd rowANDAMAN & NICOBAR ISLANDS
3rd rowANDAMAN & NICOBAR ISLANDS
4th rowANDAMAN & NICOBAR ISLANDS
5th rowANDAMAN & NICOBAR ISLANDS

Common Values

ValueCountFrequency (%)
WEST MADHYA PRADESH 115
 
2.8%
EAST RAJASTHAN 115
 
2.8%
COASTAL KARNATAKA 115
 
2.8%
TAMIL NADU 115
 
2.8%
RAYALSEEMA 115
 
2.8%
TELANGANA 115
 
2.8%
COASTAL ANDHRA PRADESH 115
 
2.8%
CHHATTISGARH 115
 
2.8%
VIDARBHA 115
 
2.8%
MATATHWADA 115
 
2.8%
Other values (26) 2966
72.1%

Length

2023-07-28T17:54:44.853634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
800
 
8.5%
pradesh 787
 
8.4%
west 575
 
6.1%
east 345
 
3.7%
karnataka 345
 
3.7%
madhya 345
 
3.7%
uttar 230
 
2.5%
interior 230
 
2.5%
bengal 230
 
2.5%
coastal 230
 
2.5%
Other values (45) 5256
56.1%

Most occurring characters

ValueCountFrequency (%)
A 12896
20.3%
5257
 
8.3%
R 4674
 
7.4%
H 4448
 
7.0%
T 4255
 
6.7%
S 3996
 
6.3%
N 3527
 
5.6%
E 3315
 
5.2%
I 2635
 
4.1%
D 2386
 
3.8%
Other values (15) 16109
25.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 57441
90.5%
Space Separator 5257
 
8.3%
Other Punctuation 800
 
1.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 12896
22.5%
R 4674
 
8.1%
H 4448
 
7.7%
T 4255
 
7.4%
S 3996
 
7.0%
N 3527
 
6.1%
E 3315
 
5.8%
I 2635
 
4.6%
D 2386
 
4.2%
M 2065
 
3.6%
Other values (13) 13244
23.1%
Space Separator
ValueCountFrequency (%)
5257
100.0%
Other Punctuation
ValueCountFrequency (%)
& 800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57441
90.5%
Common 6057
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 12896
22.5%
R 4674
 
8.1%
H 4448
 
7.7%
T 4255
 
7.4%
S 3996
 
7.0%
N 3527
 
6.1%
E 3315
 
5.8%
I 2635
 
4.6%
D 2386
 
4.2%
M 2065
 
3.6%
Other values (13) 13244
23.1%
Common
ValueCountFrequency (%)
5257
86.8%
& 800
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 12896
20.3%
5257
 
8.3%
R 4674
 
7.4%
H 4448
 
7.0%
T 4255
 
6.7%
S 3996
 
6.3%
N 3527
 
5.6%
E 3315
 
5.2%
I 2635
 
4.1%
D 2386
 
3.8%
Other values (15) 16109
25.4%

YEAR
Real number (ℝ)

Distinct115
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1958.2187
Minimum1901
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:45.054633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1901
5-th percentile1906
Q11930
median1958
Q31987
95-th percentile2010
Maximum2015
Range114
Interquartile range (IQR)57

Descriptive statistics

Standard deviation33.140898
Coefficient of variation (CV)0.016924003
Kurtosis-1.1981968
Mean1958.2187
Median Absolute Deviation (MAD)29
Skewness-0.0057225214
Sum8060028
Variance1098.3191
MonotonicityNot monotonic
2023-07-28T17:54:45.270100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1963 36
 
0.9%
2002 36
 
0.9%
1976 36
 
0.9%
1975 36
 
0.9%
1974 36
 
0.9%
1973 36
 
0.9%
1972 36
 
0.9%
1971 36
 
0.9%
1970 36
 
0.9%
1969 36
 
0.9%
Other values (105) 3756
91.3%
ValueCountFrequency (%)
1901 35
0.9%
1902 35
0.9%
1903 35
0.9%
1904 35
0.9%
1905 35
0.9%
1906 35
0.9%
1907 35
0.9%
1908 35
0.9%
1909 34
0.8%
1910 35
0.9%
ValueCountFrequency (%)
2015 36
0.9%
2014 36
0.9%
2013 36
0.9%
2012 36
0.9%
2011 36
0.9%
2010 36
0.9%
2009 36
0.9%
2008 36
0.9%
2007 36
0.9%
2006 36
0.9%

JAN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct802
Distinct (%)19.5%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean18.95732
Minimum0
Maximum583.7
Zeros608
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:45.490454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6
median6
Q322.2
95-th percentile83.035
Maximum583.7
Range583.7
Interquartile range (IQR)21.6

Descriptive statistics

Standard deviation33.585371
Coefficient of variation (CV)1.7716308
Kurtosis34.839815
Mean18.95732
Median Absolute Deviation (MAD)6
Skewness4.3086818
Sum77952.5
Variance1127.9772
MonotonicityNot monotonic
2023-07-28T17:54:45.983161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 608
 
14.8%
0.1 133
 
3.2%
0.2 101
 
2.5%
0.3 62
 
1.5%
0.4 56
 
1.4%
0.5 50
 
1.2%
0.7 44
 
1.1%
1.2 39
 
0.9%
1.3 39
 
0.9%
1 38
 
0.9%
Other values (792) 2942
71.5%
ValueCountFrequency (%)
0 608
14.8%
0.1 133
 
3.2%
0.2 101
 
2.5%
0.3 62
 
1.5%
0.4 56
 
1.4%
0.5 50
 
1.2%
0.6 37
 
0.9%
0.7 44
 
1.1%
0.8 38
 
0.9%
0.9 32
 
0.8%
ValueCountFrequency (%)
583.7 1
< 0.1%
367.8 1
< 0.1%
344.8 1
< 0.1%
308.4 1
< 0.1%
296 1
< 0.1%
276.9 1
< 0.1%
265.9 1
< 0.1%
262.8 1
< 0.1%
246.3 1
< 0.1%
245.5 1
< 0.1%

FEB
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct898
Distinct (%)21.8%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean21.805325
Minimum0
Maximum403.5
Zeros645
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:46.205187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6
median6.7
Q326.8
95-th percentile96.72
Maximum403.5
Range403.5
Interquartile range (IQR)26.2

Descriptive statistics

Standard deviation35.909488
Coefficient of variation (CV)1.646822
Kurtosis12.32666
Mean21.805325
Median Absolute Deviation (MAD)6.7
Skewness2.9810027
Sum89685.3
Variance1289.4913
MonotonicityNot monotonic
2023-07-28T17:54:46.414719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 645
 
15.7%
0.1 134
 
3.3%
0.2 84
 
2.0%
0.5 59
 
1.4%
0.4 53
 
1.3%
0.7 48
 
1.2%
0.3 47
 
1.1%
0.6 37
 
0.9%
1.1 36
 
0.9%
1.5 34
 
0.8%
Other values (888) 2936
71.3%
ValueCountFrequency (%)
0 645
15.7%
0.1 134
 
3.3%
0.2 84
 
2.0%
0.3 47
 
1.1%
0.4 53
 
1.3%
0.5 59
 
1.4%
0.6 37
 
0.9%
0.7 48
 
1.2%
0.8 32
 
0.8%
0.9 25
 
0.6%
ValueCountFrequency (%)
403.5 1
< 0.1%
306.3 1
< 0.1%
297.2 1
< 0.1%
271.8 1
< 0.1%
267.2 1
< 0.1%
250.6 1
< 0.1%
243.1 1
< 0.1%
228.9 1
< 0.1%
222.9 1
< 0.1%
221 1
< 0.1%

MAR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct978
Distinct (%)23.8%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean27.359197
Minimum0
Maximum605.6
Zeros452
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:46.634152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7.8
Q331.3
95-th percentile126.3
Maximum605.6
Range605.6
Interquartile range (IQR)30.3

Descriptive statistics

Standard deviation46.959424
Coefficient of variation (CV)1.7164036
Kurtosis15.213621
Mean27.359197
Median Absolute Deviation (MAD)7.7
Skewness3.2094297
Sum112446.3
Variance2205.1875
MonotonicityNot monotonic
2023-07-28T17:54:46.836779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 452
 
11.0%
0.1 141
 
3.4%
0.2 90
 
2.2%
0.6 65
 
1.6%
0.3 52
 
1.3%
0.5 50
 
1.2%
0.4 48
 
1.2%
0.7 44
 
1.1%
0.8 38
 
0.9%
0.9 34
 
0.8%
Other values (968) 3096
75.2%
ValueCountFrequency (%)
0 452
11.0%
0.1 141
 
3.4%
0.2 90
 
2.2%
0.3 52
 
1.3%
0.4 48
 
1.2%
0.5 50
 
1.2%
0.6 65
 
1.6%
0.7 44
 
1.1%
0.8 38
 
0.9%
0.9 34
 
0.8%
ValueCountFrequency (%)
605.6 1
< 0.1%
382 1
< 0.1%
381.8 1
< 0.1%
353.9 1
< 0.1%
341.4 1
< 0.1%
340.5 1
< 0.1%
318.8 1
< 0.1%
309.9 1
< 0.1%
309.8 1
< 0.1%
306.5 1
< 0.1%

APR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1234
Distinct (%)30.0%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean43.127432
Minimum0
Maximum595.1
Zeros219
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:47.052249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median15.7
Q349.95
95-th percentile184.89
Maximum595.1
Range595.1
Interquartile range (IQR)46.95

Descriptive statistics

Standard deviation67.831168
Coefficient of variation (CV)1.572808
Kurtosis10.237233
Mean43.127432
Median Absolute Deviation (MAD)14.9
Skewness2.8297277
Sum177340
Variance4601.0673
MonotonicityNot monotonic
2023-07-28T17:54:47.254505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 219
 
5.3%
0.1 79
 
1.9%
0.2 68
 
1.7%
0.7 43
 
1.0%
0.4 42
 
1.0%
0.3 40
 
1.0%
0.6 34
 
0.8%
0.5 34
 
0.8%
1 31
 
0.8%
1.1 27
 
0.7%
Other values (1224) 3495
84.9%
ValueCountFrequency (%)
0 219
5.3%
0.1 79
 
1.9%
0.2 68
 
1.7%
0.3 40
 
1.0%
0.4 42
 
1.0%
0.5 34
 
0.8%
0.6 34
 
0.8%
0.7 43
 
1.0%
0.8 25
 
0.6%
0.9 20
 
0.5%
ValueCountFrequency (%)
595.1 1
< 0.1%
565.4 1
< 0.1%
560.2 1
< 0.1%
502.7 1
< 0.1%
492.3 1
< 0.1%
466.9 1
< 0.1%
460.2 1
< 0.1%
457.1 1
< 0.1%
441.1 1
< 0.1%
441 1
< 0.1%

MAY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1731
Distinct (%)42.1%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean85.745417
Minimum0
Maximum1168.6
Zeros82
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:47.467287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q18.6
median36.6
Q397.2
95-th percentile352.28
Maximum1168.6
Range1168.6
Interquartile range (IQR)88.6

Descriptive statistics

Standard deviation123.2349
Coefficient of variation (CV)1.4372185
Kurtosis7.0140469
Mean85.745417
Median Absolute Deviation (MAD)32.2
Skewness2.3899824
Sum352670.9
Variance15186.841
MonotonicityNot monotonic
2023-07-28T17:54:47.674733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 82
 
2.0%
0.1 30
 
0.7%
0.3 28
 
0.7%
0.2 28
 
0.7%
0.7 28
 
0.7%
1.3 21
 
0.5%
0.4 20
 
0.5%
0.9 19
 
0.5%
1.1 18
 
0.4%
2.1 17
 
0.4%
Other values (1721) 3822
92.9%
ValueCountFrequency (%)
0 82
2.0%
0.1 30
 
0.7%
0.2 28
 
0.7%
0.3 28
 
0.7%
0.4 20
 
0.5%
0.5 15
 
0.4%
0.6 17
 
0.4%
0.7 28
 
0.7%
0.8 16
 
0.4%
0.9 19
 
0.5%
ValueCountFrequency (%)
1168.6 1
< 0.1%
973.1 1
< 0.1%
861.1 1
< 0.1%
743 1
< 0.1%
738.8 1
< 0.1%
729.3 1
< 0.1%
728.4 1
< 0.1%
726.8 1
< 0.1%
705.3 1
< 0.1%
699.5 1
< 0.1%

JUN
Real number (ℝ)

Distinct2722
Distinct (%)66.2%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean230.23444
Minimum0.4
Maximum1609.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:47.889911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile21.45
Q170.35
median138.7
Q3305.15
95-th percentile746.8
Maximum1609.9
Range1609.5
Interquartile range (IQR)234.8

Descriptive statistics

Standard deviation234.71076
Coefficient of variation (CV)1.0194424
Kurtosis2.9444235
Mean230.23444
Median Absolute Deviation (MAD)85.3
Skewness1.7390808
Sum946493.8
Variance55089.14
MonotonicityNot monotonic
2023-07-28T17:54:48.097434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.6 6
 
0.1%
92.2 6
 
0.1%
163.2 6
 
0.1%
129.8 6
 
0.1%
75.6 6
 
0.1%
72.4 6
 
0.1%
50.6 6
 
0.1%
63.1 6
 
0.1%
125.1 6
 
0.1%
48.6 6
 
0.1%
Other values (2712) 4051
98.4%
ValueCountFrequency (%)
0.4 1
< 0.1%
0.6 1
< 0.1%
1.1 1
< 0.1%
1.2 1
< 0.1%
1.4 1
< 0.1%
1.6 1
< 0.1%
2 1
< 0.1%
2.1 1
< 0.1%
2.3 1
< 0.1%
2.6 1
< 0.1%
ValueCountFrequency (%)
1609.9 1
< 0.1%
1511.3 1
< 0.1%
1432.8 1
< 0.1%
1361.6 1
< 0.1%
1327.8 1
< 0.1%
1233.2 1
< 0.1%
1227 1
< 0.1%
1221.1 1
< 0.1%
1212.6 1
< 0.1%
1193.3 1
< 0.1%

JUL
Real number (ℝ)

Distinct3050
Distinct (%)74.2%
Missing7
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean347.21433
Minimum0
Maximum2362.8
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:48.325289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile69.48
Q1175.6
median284.8
Q3418.4
95-th percentile941.94
Maximum2362.8
Range2362.8
Interquartile range (IQR)242.8

Descriptive statistics

Standard deviation269.53967
Coefficient of variation (CV)0.77629188
Kurtosis5.7361224
Mean347.21433
Median Absolute Deviation (MAD)119.9
Skewness2.1008871
Sum1426703.7
Variance72651.632
MonotonicityNot monotonic
2023-07-28T17:54:48.630784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170.2 5
 
0.1%
321.3 5
 
0.1%
338.8 5
 
0.1%
115 5
 
0.1%
215.9 5
 
0.1%
248.6 5
 
0.1%
268.8 4
 
0.1%
318.7 4
 
0.1%
215.4 4
 
0.1%
194.8 4
 
0.1%
Other values (3040) 4063
98.7%
(Missing) 7
 
0.2%
ValueCountFrequency (%)
0 1
< 0.1%
2.4 1
< 0.1%
3.2 1
< 0.1%
5.1 1
< 0.1%
7.7 1
< 0.1%
8 1
< 0.1%
11.8 1
< 0.1%
12.6 1
< 0.1%
13.5 1
< 0.1%
15.9 1
< 0.1%
ValueCountFrequency (%)
2362.8 1
< 0.1%
1904.9 1
< 0.1%
1884.9 1
< 0.1%
1867.2 1
< 0.1%
1778.9 1
< 0.1%
1748.2 1
< 0.1%
1727 1
< 0.1%
1643.9 1
< 0.1%
1630.1 1
< 0.1%
1627.2 1
< 0.1%

AUG
Real number (ℝ)

Distinct2913
Distinct (%)70.8%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean290.2635
Minimum0
Maximum1664.6
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:48.985035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile63.6
Q1155.975
median259.4
Q3377.8
95-th percentile631.4
Maximum1664.6
Range1664.6
Interquartile range (IQR)221.825

Descriptive statistics

Standard deviation188.77048
Coefficient of variation (CV)0.65034177
Kurtosis4.9422689
Mean290.2635
Median Absolute Deviation (MAD)109
Skewness1.6552052
Sum1193563.5
Variance35634.293
MonotonicityNot monotonic
2023-07-28T17:54:49.344740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226.7 6
 
0.1%
152.2 5
 
0.1%
380.9 5
 
0.1%
290.9 5
 
0.1%
118.1 5
 
0.1%
356.4 5
 
0.1%
175.6 5
 
0.1%
183.2 5
 
0.1%
312.4 5
 
0.1%
235.5 5
 
0.1%
Other values (2903) 4061
98.7%
ValueCountFrequency (%)
0 1
< 0.1%
0.6 1
< 0.1%
1.1 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
6.8 2
< 0.1%
9.4 1
< 0.1%
10.6 1
< 0.1%
12.3 2
< 0.1%
13.4 2
< 0.1%
ValueCountFrequency (%)
1664.6 1
< 0.1%
1579.1 1
< 0.1%
1414.7 1
< 0.1%
1405.9 1
< 0.1%
1347.2 1
< 0.1%
1331.2 1
< 0.1%
1299.8 1
< 0.1%
1247.4 1
< 0.1%
1231.8 1
< 0.1%
1201.9 1
< 0.1%

SEP
Real number (ℝ)

Distinct2632
Distinct (%)64.0%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean197.36192
Minimum0.1
Maximum1222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:49.698031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile24.4
Q1100.525
median173.9
Q3265.8
95-th percentile455.875
Maximum1222
Range1221.9
Interquartile range (IQR)165.275

Descriptive statistics

Standard deviation135.40834
Coefficient of variation (CV)0.68609154
Kurtosis3.1585847
Mean197.36192
Median Absolute Deviation (MAD)80.85
Skewness1.3133653
Sum811157.5
Variance18335.42
MonotonicityNot monotonic
2023-07-28T17:54:50.083336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160.8 7
 
0.2%
131.7 6
 
0.1%
88 6
 
0.1%
123.9 5
 
0.1%
139.6 5
 
0.1%
113.6 5
 
0.1%
100.4 5
 
0.1%
146.7 5
 
0.1%
230.5 5
 
0.1%
288.1 5
 
0.1%
Other values (2622) 4056
98.5%
(Missing) 6
 
0.1%
ValueCountFrequency (%)
0.1 1
< 0.1%
0.2 1
< 0.1%
0.5 2
< 0.1%
0.6 2
< 0.1%
0.8 2
< 0.1%
1 2
< 0.1%
1.3 1
< 0.1%
1.6 1
< 0.1%
1.7 1
< 0.1%
1.8 1
< 0.1%
ValueCountFrequency (%)
1222 1
< 0.1%
1108.9 1
< 0.1%
1034.8 1
< 0.1%
877.3 1
< 0.1%
868.9 1
< 0.1%
820.4 1
< 0.1%
799.9 1
< 0.1%
791.4 1
< 0.1%
788.8 1
< 0.1%
782.3 1
< 0.1%

OCT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1939
Distinct (%)47.2%
Missing7
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean95.507009
Minimum0
Maximum948.3
Zeros85
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:50.440044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q114.6
median65.2
Q3148.4
95-th percentile294.7
Maximum948.3
Range948.3
Interquartile range (IQR)133.8

Descriptive statistics

Standard deviation99.519134
Coefficient of variation (CV)1.0420087
Kurtosis3.1788208
Mean95.507009
Median Absolute Deviation (MAD)57.9
Skewness1.4987117
Sum392438.3
Variance9904.058
MonotonicityNot monotonic
2023-07-28T17:54:50.797017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 85
 
2.1%
0.1 42
 
1.0%
0.2 41
 
1.0%
0.4 28
 
0.7%
0.3 25
 
0.6%
1.1 23
 
0.6%
0.5 21
 
0.5%
1.4 19
 
0.5%
0.6 17
 
0.4%
1.5 17
 
0.4%
Other values (1929) 3791
92.1%
ValueCountFrequency (%)
0 85
2.1%
0.1 42
1.0%
0.2 41
1.0%
0.3 25
 
0.6%
0.4 28
 
0.7%
0.5 21
 
0.5%
0.6 17
 
0.4%
0.7 14
 
0.3%
0.8 11
 
0.3%
0.9 17
 
0.4%
ValueCountFrequency (%)
948.3 1
< 0.1%
669.4 1
< 0.1%
633.5 1
< 0.1%
618.6 1
< 0.1%
567.9 1
< 0.1%
543.2 1
< 0.1%
537.2 1
< 0.1%
527.2 1
< 0.1%
513.5 1
< 0.1%
511.7 1
< 0.1%

NOV
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1239
Distinct (%)30.2%
Missing11
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean39.866163
Minimum0
Maximum648.9
Zeros651
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:51.148077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.7
median9.5
Q346.1
95-th percentile191.56
Maximum648.9
Range648.9
Interquartile range (IQR)45.4

Descriptive statistics

Standard deviation68.68541
Coefficient of variation (CV)1.7228999
Kurtosis9.7031622
Mean39.866163
Median Absolute Deviation (MAD)9.5
Skewness2.7857932
Sum163650.6
Variance4717.6855
MonotonicityNot monotonic
2023-07-28T17:54:51.492792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 651
 
15.8%
0.1 114
 
2.8%
0.2 73
 
1.8%
0.3 65
 
1.6%
0.4 45
 
1.1%
0.7 39
 
0.9%
1 36
 
0.9%
0.6 36
 
0.9%
0.5 35
 
0.9%
0.8 29
 
0.7%
Other values (1229) 2982
72.4%
ValueCountFrequency (%)
0 651
15.8%
0.1 114
 
2.8%
0.2 73
 
1.8%
0.3 65
 
1.6%
0.4 45
 
1.1%
0.5 35
 
0.9%
0.6 36
 
0.9%
0.7 39
 
0.9%
0.8 29
 
0.7%
0.9 29
 
0.7%
ValueCountFrequency (%)
648.9 1
< 0.1%
583 1
< 0.1%
558.2 1
< 0.1%
541 1
< 0.1%
452.9 1
< 0.1%
418.4 1
< 0.1%
416.7 1
< 0.1%
413.9 1
< 0.1%
404.9 1
< 0.1%
383.8 1
< 0.1%

DEC
Real number (ℝ)

Distinct801
Distinct (%)19.5%
Missing10
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean18.87058
Minimum0
Maximum617.5
Zeros884
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:51.857428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median3
Q317.5
95-th percentile89.15
Maximum617.5
Range617.5
Interquartile range (IQR)17.4

Descriptive statistics

Standard deviation42.369611
Coefficient of variation (CV)2.2452734
Kurtosis41.505321
Mean18.87058
Median Absolute Deviation (MAD)3
Skewness5.2354425
Sum77482.6
Variance1795.184
MonotonicityNot monotonic
2023-07-28T17:54:52.483325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 884
 
21.5%
0.1 179
 
4.3%
0.2 111
 
2.7%
0.3 97
 
2.4%
0.5 64
 
1.6%
0.4 63
 
1.5%
0.6 59
 
1.4%
0.9 38
 
0.9%
0.8 38
 
0.9%
1.1 36
 
0.9%
Other values (791) 2537
61.6%
ValueCountFrequency (%)
0 884
21.5%
0.1 179
 
4.3%
0.2 111
 
2.7%
0.3 97
 
2.4%
0.4 63
 
1.5%
0.5 64
 
1.6%
0.6 59
 
1.4%
0.7 34
 
0.8%
0.8 38
 
0.9%
0.9 38
 
0.9%
ValueCountFrequency (%)
617.5 1
< 0.1%
560.7 1
< 0.1%
500.7 1
< 0.1%
500.4 1
< 0.1%
445.2 1
< 0.1%
434.8 1
< 0.1%
384.9 1
< 0.1%
359.6 1
< 0.1%
344.7 1
< 0.1%
334.1 1
< 0.1%

ANNUAL
Real number (ℝ)

Distinct3712
Distinct (%)90.8%
Missing26
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1411.0089
Minimum62.3
Maximum6331.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:52.847801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum62.3
5-th percentile419.195
Q1804.5
median1121.3
Q31644.775
95-th percentile3270.975
Maximum6331.1
Range6268.8
Interquartile range (IQR)840.275

Descriptive statistics

Standard deviation903.84656
Coefficient of variation (CV)0.64056759
Kurtosis1.392618
Mean1411.0089
Median Absolute Deviation (MAD)379.1
Skewness1.3195216
Sum5771026.4
Variance816938.61
MonotonicityNot monotonic
2023-07-28T17:54:53.195526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1024.6 4
 
0.1%
790.5 4
 
0.1%
770.3 4
 
0.1%
1114.2 3
 
0.1%
690.1 3
 
0.1%
1245 3
 
0.1%
842.3 3
 
0.1%
1240.4 3
 
0.1%
1061.6 3
 
0.1%
1350.4 3
 
0.1%
Other values (3702) 4057
98.6%
(Missing) 26
 
0.6%
ValueCountFrequency (%)
62.3 1
< 0.1%
92.4 1
< 0.1%
92.7 1
< 0.1%
114.4 1
< 0.1%
117.6 1
< 0.1%
119 1
< 0.1%
137.4 1
< 0.1%
138.8 1
< 0.1%
140.4 1
< 0.1%
142.2 1
< 0.1%
ValueCountFrequency (%)
6331.1 1
< 0.1%
6129 1
< 0.1%
5691.4 1
< 0.1%
5553.9 1
< 0.1%
5486.3 1
< 0.1%
5272.7 1
< 0.1%
5253.2 1
< 0.1%
5063.5 1
< 0.1%
4959.3 1
< 0.1%
4874.7 1
< 0.1%

Jan-Feb
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1220
Distinct (%)29.7%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean40.747786
Minimum0
Maximum699.5
Zeros238
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:53.555384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.1
median19.2
Q350.375
95-th percentile165.9
Maximum699.5
Range699.5
Interquartile range (IQR)46.275

Descriptive statistics

Standard deviation59.308277
Coefficient of variation (CV)1.4554969
Kurtosis13.435125
Mean40.747786
Median Absolute Deviation (MAD)17.7
Skewness2.9858258
Sum167473.4
Variance3517.4717
MonotonicityNot monotonic
2023-07-28T17:54:53.923679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 238
 
5.8%
0.1 80
 
1.9%
0.2 52
 
1.3%
0.3 38
 
0.9%
0.4 32
 
0.8%
0.9 27
 
0.7%
0.5 27
 
0.7%
1 27
 
0.7%
0.7 26
 
0.6%
0.8 21
 
0.5%
Other values (1210) 3542
86.1%
ValueCountFrequency (%)
0 238
5.8%
0.1 80
 
1.9%
0.2 52
 
1.3%
0.3 38
 
0.9%
0.4 32
 
0.8%
0.5 27
 
0.7%
0.6 13
 
0.3%
0.7 26
 
0.6%
0.8 21
 
0.5%
0.9 27
 
0.7%
ValueCountFrequency (%)
699.5 1
< 0.1%
584.5 1
< 0.1%
559 1
< 0.1%
468.3 1
< 0.1%
463.9 1
< 0.1%
397.1 1
< 0.1%
379.2 1
< 0.1%
377.3 1
< 0.1%
372.2 1
< 0.1%
370.7 1
< 0.1%

Mar-May
Real number (ℝ)

Distinct2262
Distinct (%)55.1%
Missing9
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean155.90175
Minimum0
Maximum1745.8
Zeros29
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:54.291699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.2
Q124.05
median74.8
Q3196.95
95-th percentile605.78
Maximum1745.8
Range1745.8
Interquartile range (IQR)172.9

Descriptive statistics

Standard deviation201.31697
Coefficient of variation (CV)1.2913066
Kurtosis5.0997388
Mean155.90175
Median Absolute Deviation (MAD)61.3
Skewness2.1156809
Sum640288.5
Variance40528.52
MonotonicityNot monotonic
2023-07-28T17:54:54.657037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
0.7%
0.1 13
 
0.3%
8.3 11
 
0.3%
0.3 11
 
0.3%
11.5 10
 
0.2%
2.9 10
 
0.2%
0.2 10
 
0.2%
2.7 10
 
0.2%
5.6 9
 
0.2%
10 9
 
0.2%
Other values (2252) 3985
96.8%
ValueCountFrequency (%)
0 29
0.7%
0.1 13
0.3%
0.2 10
 
0.2%
0.3 11
 
0.3%
0.4 7
 
0.2%
0.5 4
 
0.1%
0.6 6
 
0.1%
0.7 2
 
< 0.1%
0.8 5
 
0.1%
0.9 6
 
0.1%
ValueCountFrequency (%)
1745.8 1
< 0.1%
1291.5 1
< 0.1%
1265.4 1
< 0.1%
1232.4 1
< 0.1%
1196.9 1
< 0.1%
1187.3 1
< 0.1%
1177.9 1
< 0.1%
1169.1 1
< 0.1%
1143.9 1
< 0.1%
1136.3 1
< 0.1%

Jun-Sep
Real number (ℝ)

Distinct3683
Distinct (%)89.7%
Missing10
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1064.7248
Minimum57.4
Maximum4536.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:55.038265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum57.4
5-th percentile300.25
Q1573.85
median881.1
Q31288.175
95-th percentile2671.25
Maximum4536.9
Range4479.5
Interquartile range (IQR)714.325

Descriptive statistics

Standard deviation707.74153
Coefficient of variation (CV)0.66471782
Kurtosis2.2918955
Mean1064.7248
Median Absolute Deviation (MAD)341.65
Skewness1.5062292
Sum4371759.9
Variance500898.07
MonotonicityNot monotonic
2023-07-28T17:54:55.394908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
573.8 4
 
0.1%
613.3 4
 
0.1%
434.3 4
 
0.1%
334.8 4
 
0.1%
466.3 3
 
0.1%
800.4 3
 
0.1%
881.5 3
 
0.1%
311.1 3
 
0.1%
959.6 3
 
0.1%
803.1 3
 
0.1%
Other values (3673) 4072
98.9%
(Missing) 10
 
0.2%
ValueCountFrequency (%)
57.4 1
< 0.1%
67.2 1
< 0.1%
73.4 1
< 0.1%
85.5 1
< 0.1%
88.5 1
< 0.1%
90.7 1
< 0.1%
94.2 1
< 0.1%
100.1 1
< 0.1%
102 1
< 0.1%
103.8 1
< 0.1%
ValueCountFrequency (%)
4536.9 1
< 0.1%
4534.5 1
< 0.1%
4206 1
< 0.1%
4155.5 1
< 0.1%
4144.4 1
< 0.1%
4121.3 1
< 0.1%
3947.1 1
< 0.1%
3919.1 1
< 0.1%
3896.1 1
< 0.1%
3886.3 1
< 0.1%

Oct-Dec
Real number (ℝ)

Distinct2389
Distinct (%)58.2%
Missing13
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean154.10049
Minimum0
Maximum1252.5
Zeros16
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2023-07-28T17:54:55.742514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.91
Q134.2
median98.2
Q3213.5
95-th percentile505.28
Maximum1252.5
Range1252.5
Interquartile range (IQR)179.3

Descriptive statistics

Standard deviation166.94266
Coefficient of variation (CV)1.0833364
Kurtosis4.567565
Mean154.10049
Median Absolute Deviation (MAD)76.3
Skewness1.9051952
Sum632274.3
Variance27869.852
MonotonicityNot monotonic
2023-07-28T17:54:56.118655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
0.4%
0.1 15
 
0.4%
0.5 13
 
0.3%
0.6 12
 
0.3%
0.7 11
 
0.3%
0.4 9
 
0.2%
0.2 9
 
0.2%
1.1 9
 
0.2%
7.1 9
 
0.2%
4.9 9
 
0.2%
Other values (2379) 3991
97.0%
(Missing) 13
 
0.3%
ValueCountFrequency (%)
0 16
0.4%
0.1 15
0.4%
0.2 9
0.2%
0.3 6
 
0.1%
0.4 9
0.2%
0.5 13
0.3%
0.6 12
0.3%
0.7 11
0.3%
0.8 9
0.2%
0.9 5
 
0.1%
ValueCountFrequency (%)
1252.5 1
< 0.1%
1158.9 1
< 0.1%
1152.9 1
< 0.1%
1133.4 1
< 0.1%
1044.5 1
< 0.1%
1037.7 1
< 0.1%
1021 1
< 0.1%
997.6 1
< 0.1%
980.3 1
< 0.1%
978.8 1
< 0.1%

Interactions

2023-07-28T17:54:39.954824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-07-28T17:53:19.574198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:24.839644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:30.566375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:35.678049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:41.304505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:46.400181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:52.013173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:55.679935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:59.125977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:02.296600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:06.627313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:11.989135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:17.135449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:22.682065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:28.032231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:33.603189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:38.218875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:42.210854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:19.868262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:25.132742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:30.871216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:35.975316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:41.554316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:46.693524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:52.317180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:55.849305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:59.302276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:02.472010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:07.198498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:12.178005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:17.426045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:22.981839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:28.327335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:33.900834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:38.401131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:42.387625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:20.161921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:25.423220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:31.174505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:36.270518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:41.845716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:46.984956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:52.619607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:56.017978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:59.476732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:02.646517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:07.479626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:12.468246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:18.019053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:23.278269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:28.886305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:34.196388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:38.688376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:42.565688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:20.458550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:25.714770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:31.477839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:36.569369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:42.137335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:47.275081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:52.925474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:56.186795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:59.653501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:02.825516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:07.760952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:12.758111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:18.313284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:23.574809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:29.181258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:34.491138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:39.267895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:42.752629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:20.758760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:26.010640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:31.786273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:36.874457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:42.433158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:47.569962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:53.227908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:56.358750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:59.840302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:03.006289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:08.050221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:13.053688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:18.623377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:23.878742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:29.480497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:34.793546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:39.444088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:42.938568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:21.057805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:26.305688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:32.081963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:37.178161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:42.730523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:47.868829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:53.476162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:56.530975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:00.024181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:03.187017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:08.337865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:13.348931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:18.921091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:24.181702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:29.783867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:35.096056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:39.619039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:43.109301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:21.340065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:26.586741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:32.371799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:37.466068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:43.011014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:48.149663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:53.649778image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:53:56.687402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:00.191232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:03.350766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:08.616678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:13.628966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:19.200176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:24.471473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:30.071074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:35.380733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-28T17:54:39.781325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-07-28T17:54:56.449248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
YEARJANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDECANNUALJan-FebMar-MayJun-SepOct-DecSUBDIVISION
YEAR1.000-0.046-0.0360.0160.0180.0110.025-0.0110.007-0.0040.0200.0170.001-0.002-0.0800.002-0.0010.0020.000
JAN-0.0461.0000.4700.4250.3190.201-0.0300.0300.1000.0330.0060.0180.2670.1700.8070.3200.0500.0610.242
FEB-0.0360.4701.0000.5690.4100.3080.0610.1100.1610.1130.029-0.0030.2490.2530.8510.4350.1360.0620.262
MAR0.0160.4250.5691.0000.5690.4510.1440.1510.1820.1770.1540.1430.3120.3420.5660.6670.1910.2020.282
APR0.0180.3190.4100.5691.0000.7110.3920.2610.2450.3080.4850.3950.3560.5430.4120.8550.3430.5290.335
MAY0.0110.2010.3080.4510.7111.0000.5220.3010.2930.4000.6200.4870.3310.6320.2940.9200.4340.6440.322
JUN0.025-0.0300.0610.1440.3920.5221.0000.6500.6150.5620.5190.3090.0590.8070.0300.4690.8310.4620.405
JUL-0.0110.0300.1100.1510.2610.3010.6501.0000.6890.5440.2790.0850.0190.7830.0820.2930.8820.2120.393
AUG0.0070.1000.1610.1820.2450.2930.6150.6891.0000.5210.2610.0750.0030.7700.1520.2930.8530.1990.343
SEP-0.0040.0330.1130.1770.3080.4000.5620.5440.5211.0000.4250.1960.0290.7010.0960.3660.7410.3580.288
OCT0.0200.0060.0290.1540.4850.6200.5190.2790.2610.4251.0000.5410.2060.5950.0330.5510.4210.9220.292
NOV0.0170.018-0.0030.1430.3950.4870.3090.0850.0750.1960.5411.0000.3570.3750.0180.4500.1850.7390.286
DEC0.0010.2670.2490.3120.3560.3310.0590.0190.0030.0290.2060.3571.0000.2140.2960.4020.0400.4060.216
ANNUAL-0.0020.1700.2530.3420.5430.6320.8070.7830.7700.7010.5950.3750.2141.0000.2520.6380.9210.5790.496
Jan-Feb-0.0800.8070.8510.5660.4120.2940.0300.0820.1520.0960.0330.0180.2960.2521.0000.4340.1150.0840.287
Mar-May0.0020.3200.4350.6670.8550.9200.4690.2930.2930.3660.5510.4500.4020.6380.4341.0000.4100.6000.410
Jun-Sep-0.0010.0500.1360.1910.3430.4340.8310.8820.8530.7410.4210.1850.0400.9210.1150.4101.0000.3470.521
Oct-Dec0.0020.0610.0620.2020.5290.6440.4620.2120.1990.3580.9220.7390.4060.5790.0840.6000.3471.0000.375
SUBDIVISION0.0000.2420.2620.2820.3350.3220.4050.3930.3430.2880.2920.2860.2160.4960.2870.4100.5210.3751.000

Missing values

2023-07-28T17:54:43.402843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-28T17:54:43.783930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-28T17:54:44.208416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SUBDIVISIONYEARJANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDECANNUALJan-FebMar-MayJun-SepOct-Dec
0ANDAMAN & NICOBAR ISLANDS190149.287.129.22.3528.8517.5365.1481.1332.6388.5558.233.63373.2136.3560.31696.3980.3
1ANDAMAN & NICOBAR ISLANDS19020.0159.812.20.0446.1537.1228.9753.7666.2197.2359.0160.53520.7159.8458.32185.9716.7
2ANDAMAN & NICOBAR ISLANDS190312.7144.00.01.0235.1479.9728.4326.7339.0181.2284.4225.02957.4156.7236.11874.0690.6
3ANDAMAN & NICOBAR ISLANDS19049.414.70.0202.4304.5495.1502.0160.1820.4222.2308.740.13079.624.1506.91977.6571.0
4ANDAMAN & NICOBAR ISLANDS19051.30.03.326.9279.5628.7368.7330.5297.0260.725.4344.72566.71.3309.71624.9630.8
5ANDAMAN & NICOBAR ISLANDS190636.60.00.00.0556.1733.3247.7320.5164.3267.8128.979.22534.436.6556.11465.8475.9
6ANDAMAN & NICOBAR ISLANDS1907110.70.0113.321.6616.3305.2443.9377.6200.4264.4648.9245.63347.9110.7751.21327.11158.9
7ANDAMAN & NICOBAR ISLANDS190820.985.10.029.0562.0693.6481.4699.9428.8170.7208.1196.93576.4106.0591.02303.7575.7
8ANDAMAN & NICOBAR ISLANDS191026.622.7206.389.3224.5472.7264.3337.4626.6208.2267.3153.52899.449.3520.11701.0629.0
9ANDAMAN & NICOBAR ISLANDS19110.08.40.0122.5327.3649.0253.0187.1464.5333.894.5247.12687.28.4449.81553.6675.4
SUBDIVISIONYEARJANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDECANNUALJan-FebMar-MayJun-SepOct-Dec
4106LAKSHADWEEP200620.10.033.00.3327.9286.9172.3150.7318.5119.1158.910.91598.620.1361.2928.4288.9
4107LAKSHADWEEP20072.54.20.222.2166.2573.4427.4294.7457.5256.147.6109.62361.66.7188.61753.0413.3
4108LAKSHADWEEP20085.519.8120.715.8180.4254.6363.9206.6108.9252.967.6130.11726.825.3316.9934.0450.6
4109LAKSHADWEEP20094.71.50.118.1162.1401.2266.4185.0145.187.4166.2132.31570.16.2180.3997.7385.9
4110LAKSHADWEEP201018.80.01.235.679.0318.9336.7335.1161.5155.4201.581.51725.218.8115.81152.2438.4
4111LAKSHADWEEP20115.12.83.185.9107.2153.6350.2254.0255.2117.4184.314.91533.77.9196.21013.0316.6
4112LAKSHADWEEP201219.20.11.676.821.2327.0231.5381.2179.8145.912.48.81405.519.399.61119.5167.1
4113LAKSHADWEEP201326.234.437.55.388.3426.2296.4154.4180.072.878.126.71426.360.6131.11057.0177.6
4114LAKSHADWEEP201453.216.14.414.957.4244.1116.1466.1132.2169.259.062.31395.069.376.7958.5290.5
4115LAKSHADWEEP20152.20.53.787.1133.1296.6257.5146.4160.4165.4231.0159.01642.92.7223.9860.9555.4